Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection
Deepfake technology poses significant threats in various domains, including politics, cybersecurity, and social media. This study uses the golden frame selection technique to present a neural network ensemble method for deepfake classification. The proposed approach optimizes computational resources...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/4/109 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850144618300047360 |
|---|---|
| author | Khrystyna Lipianina-Honcharenko Nazar Melnyk Andriy Ivasechko Mykola Telka Oleg Illiashenko |
| author_facet | Khrystyna Lipianina-Honcharenko Nazar Melnyk Andriy Ivasechko Mykola Telka Oleg Illiashenko |
| author_sort | Khrystyna Lipianina-Honcharenko |
| collection | DOAJ |
| description | Deepfake technology poses significant threats in various domains, including politics, cybersecurity, and social media. This study uses the golden frame selection technique to present a neural network ensemble method for deepfake classification. The proposed approach optimizes computational resources by extracting the most informative video frames, improving detection accuracy. We integrate multiple deep learning models, including ResNet50, EfficientNetB0, Xception, InceptionV3, and Facenet, with an XGBoost meta-model for enhanced classification performance. Experimental results demonstrate a 91% accuracy rate, outperforming traditional deepfake detection models. Additionally, feature importance analysis using Grad-CAM highlights how different architectures focus on distinct facial regions, enhancing overall model interpretability. The findings contribute to of robust and efficient deepfake detection techniques, with potential applications in digital forensics, media verification, and cybersecurity. |
| format | Article |
| id | doaj-art-3ad9b9b53b5d4ddba663d3e31028274a |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-3ad9b9b53b5d4ddba663d3e31028274a2025-08-20T02:28:19ZengMDPI AGBig Data and Cognitive Computing2504-22892025-04-019410910.3390/bdcc9040109Neural Network Ensemble Method for Deepfake Classification Using Golden Frame SelectionKhrystyna Lipianina-Honcharenko0Nazar Melnyk1Andriy Ivasechko2Mykola Telka3Oleg Illiashenko4Department of Information and Computing Systems and Control, Faculty of Computer Information Technologies, West Ukrainian National University, 46000 Ternopil, UkraineDepartment of Information and Computing Systems and Control, Faculty of Computer Information Technologies, West Ukrainian National University, 46000 Ternopil, UkraineDepartment of Information and Computing Systems and Control, Faculty of Computer Information Technologies, West Ukrainian National University, 46000 Ternopil, UkraineDepartment of Information and Computing Systems and Control, Faculty of Computer Information Technologies, West Ukrainian National University, 46000 Ternopil, UkraineDepartment of Computer Systems, Networks and Cybersecurity, Faculty of Radio Electronics, Computer Systems and Infocommunications, National Aerospace University “KhAI”, 61000 Kharkiv, UkraineDeepfake technology poses significant threats in various domains, including politics, cybersecurity, and social media. This study uses the golden frame selection technique to present a neural network ensemble method for deepfake classification. The proposed approach optimizes computational resources by extracting the most informative video frames, improving detection accuracy. We integrate multiple deep learning models, including ResNet50, EfficientNetB0, Xception, InceptionV3, and Facenet, with an XGBoost meta-model for enhanced classification performance. Experimental results demonstrate a 91% accuracy rate, outperforming traditional deepfake detection models. Additionally, feature importance analysis using Grad-CAM highlights how different architectures focus on distinct facial regions, enhancing overall model interpretability. The findings contribute to of robust and efficient deepfake detection techniques, with potential applications in digital forensics, media verification, and cybersecurity.https://www.mdpi.com/2504-2289/9/4/109deepfake detectionneural network ensemblegolden frame selectionResNet50EfficientNetB0Xception |
| spellingShingle | Khrystyna Lipianina-Honcharenko Nazar Melnyk Andriy Ivasechko Mykola Telka Oleg Illiashenko Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection Big Data and Cognitive Computing deepfake detection neural network ensemble golden frame selection ResNet50 EfficientNetB0 Xception |
| title | Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection |
| title_full | Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection |
| title_fullStr | Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection |
| title_full_unstemmed | Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection |
| title_short | Neural Network Ensemble Method for Deepfake Classification Using Golden Frame Selection |
| title_sort | neural network ensemble method for deepfake classification using golden frame selection |
| topic | deepfake detection neural network ensemble golden frame selection ResNet50 EfficientNetB0 Xception |
| url | https://www.mdpi.com/2504-2289/9/4/109 |
| work_keys_str_mv | AT khrystynalipianinahoncharenko neuralnetworkensemblemethodfordeepfakeclassificationusinggoldenframeselection AT nazarmelnyk neuralnetworkensemblemethodfordeepfakeclassificationusinggoldenframeselection AT andriyivasechko neuralnetworkensemblemethodfordeepfakeclassificationusinggoldenframeselection AT mykolatelka neuralnetworkensemblemethodfordeepfakeclassificationusinggoldenframeselection AT olegilliashenko neuralnetworkensemblemethodfordeepfakeclassificationusinggoldenframeselection |